where \(\alpha \)  denotes the momentum and \(\eta \)  stands for the learning rate. To obtain the terms \(P(\tilde{\textbf {h}}|\tilde{\textbf {v}})\)  and \(\tilde{\textbf {v}}\) , one can perform the Contrastive Divergence [1]} technique, which basically ends up performing Gibbs sampling using the training data as the visible units. In short, Equations REF , REF  and REF  employ the well-known Gradient Descent as the optimization algorithm. The additional term \(\Phi \)  in Equation REF  is used to control the values of matrix \(\textbf {W}\)  during the convergence process, and it is formulated as follows:
\(\Phi = -\lambda \textbf {W}^t+\alpha \Delta \textbf {W}^{t-1},\) 
